Combination of real-time analytics and automation

ABSTRACT

Real-time speech analytics (RTSA) provides maintaining real-time speech conditions, rules, and triggers, and real-time actions and alerts to take. A call between a user and an agent is received at an agent computing device. The call is monitored to detect in the call one of the real-time speech conditions, rules, and triggers. Based on the detection, at least one real-time action and/or alert is initiated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/407,891, filed on May 9, 2019, entitled “COMBINATION OF REAL-TIMEANALYTICS AND AUTOMATION,” the contents of which are hereby incorporatedby reference in their entirety.

BACKGROUND

Speech analytics is the process of analyzing recorded calls to gathercustomer information. Speech analytics can provide analysis of recordedphone conversations between a company and its customers.

Real-time speech analytics listen to voice interactions as they happen.Real-time speech analytics can analyze the words spoken on a call inreal-time. Conventional real-time speech analytics are basic and simple.Moreover, conventional analytical or automation systems operate eitherin silos or require heavy, expensive, and complex custom integration towork together.

SUMMARY

Real-time speech analytics (RTSA) systems and methods maintain real-timespeech conditions, rules, and triggers, and real-time actions and alertsto take. A call between a user and an agent is received at an agentcomputing device. The call is monitored to detect in the call one of thereal-time speech conditions, rules, and triggers. Based on thedetection, at least one real-time action and/or alert is initiated. Thesystems and methods provided herein allow for rapid integration andrules configuration across systems without requiring custom code. Theseinclude real-time biometric analytics, real-time speech analytics,real-time desktop/activity analytics, and desktop pop-ups/real-timeguidance/invocation of desktop commands or web-services for automatedaction.

Elements of the analytics and automation solution are provided as wellas the ability to tie them together in user configurable ways to solvevaried and evolving business problems.

An embodiment that is described herein provides a real-time speechanalytics (RTSA) system that comprises a rules module configured tomaintain a plurality of rules pertaining to a call; a detection moduleconfigured to listen to the call according to the plurality of rules andto detect that one of the plurality of rules is triggered as a match oras an event; an analysis module configured to analyze the match or theevent and determine at least one of an action or an alert to perform;and an action and alerts module configured to receive an instructionfrom the analysis module pertaining to the at least one of the action orthe alert to perform, and configured to perform the at least one of theaction or the alert.

In another embodiment, a system is provided that comprises an agentcomputing device configured to receive a call from a user computingdevice; and a computing device comprising a real-time speech analytics(RTSA) engine configured to monitor and analyze the call in real-time todetect at least one of predetermined words, predetermined phrases, andsentiment, and to guide an interaction during the call using one or moreautomated interventions.

In another embodiment, a method comprises maintaining, at a detectionmodule, a plurality of real-time speech conditions, rules, and triggers;maintaining, at an analysis module, a plurality of real-time actions andalerts to take; receiving a call at an agent computing device; detectingin the call one of the plurality of real-time speech conditions, rules,and triggers; and initiating one or more of the plurality of real-timeactions and alerts based on the detected one of the plurality ofreal-time speech conditions, rules, and triggers.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating theembodiments, there is shown in the drawings example constructions of theembodiments; however, the embodiments are not limited to the specificmethods and instrumentalities disclosed. In the drawings:

FIG. 1 is an illustration of an exemplary environment for real-timespeech analytics;

FIG. 2 is a diagram of an example real-time speech analytics engine;

FIG. 3 is an operational flow of an implementation of a method forreal-time speech analytics;

FIG. 4 is an operational flow of another implementation of a method forreal-time speech analytics; and

FIG. 5 shows an exemplary computing environment in which exampleembodiments and aspects may be implemented.

DETAILED DESCRIPTION

FIG. 1 is an illustration of an exemplary environment 100 for real-timespeech analytics. A user 102, using a user computing device 105 withvoice capability or using a telephone 106 contacts an entity through anetwork 108. More particularly, the user 102 contacts an agent 152 (orrepresentative, employee, associate, etc.) of a company using the usercomputing device 105 or the telephone 106 in communication with an agentcomputing device 155 via the network 108. The agent computing device 155has human voice capability. Additionally or alternatively, the agentcomputing device 155 has virtual agent voice capability.

A computing device 110 may be in communication with the agent computingdevice 155 to monitor the speech in a voice call (i.e., theconversation) between the user computing device 105 (or the telephone106) and the agent computing device 155. The computing device 110 may beimplemented in, or embodied in, a desktop analytics product or in aspeech analytics product, in some implementations. Depending on theimplementation, an output device 195 may be in communication with thecomputing device 110, in either a wired connection or a wirelessconnection.

The network 108 may be a variety of network types including the publicswitched telephone network (PSTN), a cellular telephone network, and apacket switched network (e.g., the Internet). Although only one usercomputing device 105/telephone 106, one agent computing device 155, onecomputing device 110, and one output device 195 are shown in FIG. 1,there is no limit to the number of computing devices 105, 155, 110,telephones 106, and output devices 195 that may be supported.

The user computing device 105, the agent computing device 155, thecomputing device 110, and the output device 195 may each be implementedusing a variety of computing devices such as smartphones, desktopcomputers, laptop computers, tablets, set top boxes, vehicle navigationsystems, and video game consoles. Other types of computing devices maybe supported. A suitable computing device is illustrated in FIG. 5 asthe computing device 500.

In some implementations, the computing device 110 comprises a callrecording engine 115, a real-time biometrics engine 120, a speechanalytics (SA) engine 125, a desktop and process analytics (DPA) engine130, and a real-time speech analytics (RTSA) engine 135. As describedfurther herein, the computing device 110 and its various engines 115,120, 125, 130, and 135 assist the agent 152 in providing better serviceand information to the user 102. The computing device 110 recognizesconditions based on the conversation between the user 102 and the agent152 in different ways and contexts. More complex rules and actions maybe implemented based on what the user 102 and/or the agent 152 is sayingand doing (e.g., actions they are taking) and based on the history ofthe phone call and conversation between the user 102 and the agent 152.

The call recording engine 115 captures the interaction between the usercomputing device 105 (or the telephone 106) and the agent computingdevice 155. Additionally, the call recording engine 115 may capturecomputer telephony integration (CTI) events, and also be used to setrules for analysis.

The real-time biometrics engine 120 authenticates the user 102, e.g., byanalyzing the speech of the user 102 as received from the user computingdevice 105 (or the telephone 106). The authentication is performed inreal-time when the user 102 calls the agent computing device 155. Anytype of voice or speech biometrics authentication may be used.

The SA engine 125 analyzes and automatically categorizes calls. The SAengine 125 also publishes categories to RTSA rules. The SA engine 125provides phonetic recognition and full transcription of calls, utilizingadvanced language understanding.

The DPA engine 130 incorporates desktop events at the agent computingdevice 155 for broader and richer interaction context between the user102 and the agent 152 (e.g., between the user computing device 105 andthe agent computing device 155). The DPA engine 130 may drivenotification and automation.

The RTSA engine 135 recognizes and analyzes calls and conversations inreal-time, as a conversation between the user 102 and the agent 152and/or the agent computing device 155 happens during a call. The RTSAengine 135 monitors calls and activity in real-time, to detectpredetermined words and phrases of interest as well as sentiment. Thisallows interactions between the user 102 and the agent 152 to be guided,resulting in better outcomes based on real-time analysis of call contentand desktop activity. Automated interventions are provided, e.g., toagents via on-screen automated assistance, to managers via notificationsand work-queue entries, and to analysts via reporting and post-callanalytics.

In some implementations, the RTSA engine 135 uses a real-time recorderthat listens to interactions as they happen, and based on conversationalindicators, identifies opportunities that can be used to guide or alterthe interaction towards a better outcome for the mutual benefit of boththe user 102 (i.e., the end customer) and the organization of the agent152.

In some implementations, the RTSA engine 135 is powered by the samespeech engine that is used in the SA engine 125.

The value of RTSA is significantly amplified and easier tooperationalize when it is deployed and used in tandem with speechanalytics and desktop analytics in addition to call recording. Thecombinations of these components working together add broader and richercontext relevant to the interaction by incorporating non-linguisticattributes associated with the interaction such as employee desktopactivities and events, CTI data, employee skills and other non-voicecontent related to the call. Speech analytics complements RTSA byidentifying trends, root causes, and automatic categorization of callsthat enable users to focus on specific issues and opportunities duringthe interaction. Desktop analytics adds employee desktop screeninformation and events to the linguistic attributes. These componentstogether validate the right use cases that require action in real-time.As described further herein, speech and screen analytics (speech anddesktop) are combined in some implementations. In this manner,conversation and desktop activity are blended.

FIG. 2 is a diagram 200 of an example real-time speech analytics engine,such as the real-time speech analytics engine 135 of FIG. 1. The RTSAengine 135 comprises a rules module 210, a detection module 220, ananalysis module 230, and an action and alerts module 240.

The rules module 210 maintains, receives, determines, and/or sets therules dictating which conversations will be analyzed and how thoseconversations will be analyzed. In some implementations, rules aredefined around keywords, sentiment, and other attributes. Additionallyor alternatively, the rules support the operators such as OR, AND, NEAR,and NOT and can also define conditions to identify situations where akeyword or phrase is not said.

The detection module 220 listens to the conversations according to therules set by the rules module 210, and detects when one of the rules istriggered, e.g., by one or more words or phrases and/or otherinteraction(s). Thus, the detection module 220 listens to the presenceor absence of words and phrases, and sentiment expressed. Based on therules, as the detection module 220 finds matches of spoken words andphrases of interest in the conversations, the matches are provided to,and analyzed by, the analysis module 230.

The analysis module 230 analyzes the matches and/or event that isdetected by the detection module 220. Based on the analysis, theanalysis module 230 determines one or more actions and/or alerts thatare to be performed, and provides instructions to the action and alertsmodule 240.

The action and alerts module 240 receives the instructions from theanalysis module 230 and then implements the instructions accordingly,e.g., by performing one or more predetermined actions and/or alerts,such as sending notifications to employees or supervisors in real-time,within a few seconds. Example scenarios are described herein.

A first example is directed to a life event discussion scenario. TheRTSA engine 135 ‘listens’ for discussion of a life event discussionaround a new place to live. The detection module 220 may have receivedrules from the rules module 210 to listen for terms such as “new house”,“new condo”, “new apartment”, “moving”, “bought” NEAR “house”, “address”NEAR “change”, etc., for example. When the detection module 220 ‘hears’this discussion in the speech of the user 102 (or the speech of theagent 152), the detection module 220 sends a notification to theanalysis module 230. The analysis module 230 analyzes the detectedterm(s) brought to its attention by the detection module 220, andtriggers the action and alerts module to take action, such as triggeringan address change knowledge article to be presented to the agent 152 onthe agent computing device 155.

Similarly, the user 102 may be discussing a medical condition. When theuser 102 starts speaking about the medical condition, the detectionmodule 220 of the RTSA engine 135 detects words defined for that medicalcondition, and the RTSA engine 135 provides the agent 152 with a link toa relevant article in a knowledge base, for example.

A second example is directed to a credit check disclosure scenario. TheRTSA engine 135 ‘listens’ for discussion of terms pertaining to a creditcheck (e.g., “credit” NEAR “check”). When the RTSA engine 135 ‘hears’the terms, the RTSA engine 135 recognizes that a discussion is occurringpertaining to a credit check, and notes that such a discussion isoccurring (e.g., provides an indicator to the DPA engine 130 that thediscussion is occurring). The DPA engine 130 receives the indicator anddetermines whether a credit check was discussed by the agent 152 and theuser 102 (e.g., by the DPA engine checking stored indicators). If thecredit check was not discussed, then a reminder is sent by the DPAengine 130 to the agent 152 via the agent computing device 155.Alternately, as soon as the RTSA engine 135 hears the terms, the RTSAengine 135 sends a credit check discussion reminder to the agent 152 viathe agent computing device 155.

A third example is directed to a risky or potentially fraudulenttransaction scenario. RTSA engine 135 ‘listens’ for language indicatinga potentially fraudulent transaction, such as “empty my account”,“external account”, “soon as possible”, “empty” NEAR “account”,“transfer” NEAR “money”, “cash out”, “send” NEAR “check”, etc. If theRTSA engine 135 ‘hears’ this language, then the agent 152 is notifiedimmediately via the agent computing device 155. The agent 152 mayexecute a different process depending on whether or not the user 102 isauthenticated when they try to execute the transaction.

A fourth example is directed to a follow-up queue for missed first callresolution (FCR) scenario. When a call between the user 102 and theagent 152 ends, the RTSA engine 135 ‘looks back’ over at least a portionof the call (e.g., the last 10 seconds of the call) to see if the term“call back” was mentioned (or other terms such as “call” NEAR “back”,“calling back”, etc., for example). If the term “call back” wasmentioned, then the RTSA engine 135 places the call in a follow-up queue(e.g., the call is marked for follow-up) and notifies the agent 152 viathe agent computing device 155 (e.g., a follow-up message is presentedto the agent 152).

A fifth example is directed to a customer churn and save offer scenario.The RTSA engine 135 ‘hears’ conversation indicating churn (terms such as“fed-up”, “frustrated”, “cancel my account”, “close my account” “cancel”NEAR “account”, “close out”, “close” NEAR “account”, etc., for example).When this occurs, the RTSA engine 135 alerts the DPA engine 130. The DPAengine 135 checks the user account status or other account and/or userinformation pertaining to the user 102 (e.g., the customer relationshipmanagement (CRM) software) prompts the agent 152, via the agentcomputing device 155, to make an appropriate save offer. The RTSA engine135 ‘listens’ for the offer to be presented and prompts a reminder tothe agent 152 if, and only if, the offer is not presented (the RTSAengine ‘listens’ for terms such as “discount”, “rebate”, “offer”,“credit”, etc.). The churn scenario instructs the agent 152 to present asave offer. If the agent 152 does not present the offer, then thereminder is presented to the agent.

For example, telecom companies operate in an inherently high churnmarket. The ability to retain the subscriber base is important to helpmaintain growth and lower the high cost of acquiring new customers. Inorder to minimize churn, the RTSA engine 135 monitors calls and theterms that are being mentioned on the call in real-time. Once the RTSAengine 135 detects a churn risk, for example, when a user says “I wouldlike to switch to another carrier”, the RTSA engine 135 guides the agent152 in real-time with a script and offers to attempt to stop the userfrom leaving.

FIG. 3 is an operational flow of an implementation of a method 300 forreal-time speech analytics. The method 300 may be performed by the RTSAengine 135, for example.

At 310, the recording rules are received and/or determined by the rulesmodule 210. The recording rules may be provided to the rules module 210by an administrator or other user, and/or may be determined by machinelearning and/or artificial intelligence associated with the RTSA engine135. The recording rules may be maintained, e.g., in storage of the RTSAengine 135. The rules may include which calls to record and/or monitor(e.g., between a user 102 and an agent 152).

At 320, conditions to detect are maintained, received, and/or determinedby the detection module 220. The conditions may include real-time speechconditions (e.g., one or more terms detected in speech from the user 102and/or the agent 152), rules (e.g., desktop (agent computing device 155)rules), and triggers (e.g., desktop (agent computing device 155)triggers). The conditions may be provided to the detection module 220 byan administrator or other user, and/or may be determined by machinelearning and/or artificial intelligence associated with the RTSA engine135.

At 330, actions to take are maintained, received and/or determined bythe analysis module 230. The actions may include real-time actions andalerts, such as triggering a reminder or message to the agent 152 (e.g.,via the agent computing device 155). The actions may comprise anotification, an indication, or an instruction, for example, responsiveto the conditions detected by the detection module 220. Thenotification, indication, or instruction will be used to send an actionor alert, e.g., to the agent 152 or other entity. The actions may beprovided to the analysis module 230 by an administrator or other user,and/or may be determined by machine learning and/or artificialintelligence associated with the RTSA engine 135.

At 340, the action drivers are received and/or determined by the actionand alerts module 240. The action drivers may include sending actionsto, or performing actions on behalf of, agents, employees, managers,analysts, etc., responsive to receiving a notification, indication, orinstruction from the analysis module 230. The actions may be provided tothe actions and alerts module 240 by an administrator or other user,and/or may be determined by machine learning and/or artificialintelligence associated with the RTSA engine 135.

FIG. 4 is an operational flow of another implementation of a method 400for real-time speech analytics. The method 400 may be performed by theRTSA engine 135, for example, either alone or in conjunction with otherelements of the computing device 110, for example, depending on theimplementation.

At 410, a caller (e.g., the user 102) places a call (e.g., to the agentcomputing device 155) and is authenticated, e.g., using real-timebiometrics. Any type of real-time biometrics may be implemented, such asby the real-time biometrics engine 120.

At 420, the call is recorded (e.g., by the call recording engine 115)and monitored (e.g., by the RTSA engine 135). At 430, one or more termsare detected, e.g., by the detection module 220. One or more conditionsare evaluated at 440, e.g., by the analysis module 230. Responsive tothe terms and/or conditions, at 450, one or more actions are initiated,e.g., by the action and alerts module 240.

At 460, guidance and/or alerts are provided, based on the initiatedactions, e.g., to the agent 152 (e.g., via the agent computing device155). Alternately or additionally, depending on the implementation, at470, the call is tagged for analysis and/or reporting.

Real-time speech analytics solutions can help organizations: (1) ensureregulatory compliance and compliance with regulations and policies; (2)increase customer retention; (3) increase first contact resolution; (4)increase sales and manage marketing campaigns; and (5) enhance coachingopportunities.

The value can be further enhanced by the optional addition ofperformance management (PM) and knowledge management (KM) products. PMscorecards can add employee skills and key performance indicator (KPI)information that can be used by the RTSA engine 135 (e.g., the analysismodule 230 and/or the action and alerts module 240) to only deliverguidance to employees who need them. KM can be used to deliver highlypertinent and knowledge to the employee and help accelerate theinteraction to a desirable closure.

The broader context and framework of products increases the accuracy,and therefore, the action ability of the guidance. It helps deliver thebest possible outcome for every interaction, both for the end consumerand the organization by recommending next best actions to employees andsupervisors via interaction context-based alerts and screen pop-upmessages that guide interactions to a mutually beneficial close.

FIG. 5 shows an exemplary computing environment in which exampleembodiments and aspects may be implemented. The computing deviceenvironment is only one example of a suitable computing environment andis not intended to suggest any limitation as to the scope of use orfunctionality.

Numerous other general purpose or special purpose computing devicesenvironments or configurations may be used. Examples of well-knowncomputing devices, environments, and/or configurations that may besuitable for use include, but are not limited to, personal computers,server computers, handheld or laptop devices, multiprocessor systems,microprocessor-based systems, network personal computers (PCs),minicomputers, mainframe computers, embedded systems, distributedcomputing environments that include any of the above systems or devices,and the like.

Computer-executable instructions, such as program modules, beingexecuted by a computer may be used. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data types.Distributed computing environments may be used where tasks are performedby remote processing devices that are linked through a communicationsnetwork or other data transmission medium. In a distributed computingenvironment, program modules and other data may be located in both localand remote computer storage media including memory storage devices.

With reference to FIG. 5, an exemplary system for implementing aspectsdescribed herein includes a computing device, such as computing device500. In its most basic configuration, computing device 500 typicallyincludes at least one processing unit 502 and memory 504. Depending onthe exact configuration and type of computing device, memory 504 may bevolatile (such as random access memory (RAM)), non-volatile (such asread-only memory (ROM), flash memory, etc.), or some combination of thetwo. This most basic configuration is illustrated in FIG. 5 by dashedline 506.

Computing device 500 may have additional features/functionality. Forexample, computing device 500 may include additional storage (removableand/or non-removable) including, but not limited to, magnetic or opticaldisks or tape. Such additional storage is illustrated in FIG. 5 byremovable storage 508 and non-removable storage 510.

Computing device 500 typically includes a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by the device 500 and includes both volatile and non-volatilemedia, removable and non-removable media.

Computer storage media include volatile and non-volatile, and removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules or other data. Memory 504, removable storage508, and non-removable storage 510 are all examples of computer storagemedia. Computer storage media include, but are not limited to, RAM, ROM,electrically erasable program read-only memory (EEPROM), flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bycomputing device 500. Any such computer storage media may be part ofcomputing device 500.

Computing device 500 may contain communication connection(s) 512 thatallow the device to communicate with other devices. Computing device 500may also have input device(s) 514 such as a keyboard, mouse, pen, voiceinput device, touch input device, etc. Output device(s) 516 such as adisplay, speakers, printer, etc. may also be included. All these devicesare well known in the art and need not be discussed at length here.

It should be understood that the various techniques described herein maybe implemented in connection with hardware components or softwarecomponents or, where appropriate, with a combination of both.Illustrative types of hardware components that can be used includeField-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SOCs), Complex Programmable Logic Devices(CPLDs), etc. The methods and apparatus of the presently disclosedsubject matter, or certain aspects or portions thereof, may take theform of program code (i.e., instructions) embodied in tangible media,such as floppy diskettes, CD-ROMs, hard drives, or any othermachine-readable storage medium where, when the program code is loadedinto and executed by a machine, such as a computer, the machine becomesan apparatus for practicing the presently disclosed subject matter.

In an implementation, a real-time speech analytics (RTSA) system isprovided. The RTSA system comprises a rules module configured tomaintain a plurality of rules pertaining to a call; a detection moduleconfigured to listen to the call according to the plurality of rules andto detect that one of the plurality of rules is triggered as a match oras an event; an analysis module configured to analyze the match or theevent and determine at least one of an action or an alert to perform;and an action and alerts module configured to receive an instructionfrom the analysis module pertaining to the at least one of the action orthe alert to perform, and configured to perform the at least one of theaction or the alert.

Implementations may include some or all of the following features. Thecall is between a user computing device and an agent computing device.The agent computing device has at least one of human voice capability orvirtual agent voice capability. The rules module, the detection module,the analysis module, and the action and alerts module are comprisedwithin a computing device. The plurality of rules dictate the call to beanalyzed and how the call is analyzed. The plurality of rules define aplurality of keywords to detect and sentiment to detect. The rulesmodule is further configured to at least one of receive, determine, orset the plurality of rules. The at least one of the action or the alertto perform comprises at least one of alerting an agent to a condition,alerting the agent computing device to the condition, sending aninstruction to an output device, or sending an indicator to a desktopand process analytics (DPA) engine. The at least one of the action orthe alert to perform comprises at least one of providing a knowledgebase article or link, alerting a credit check disclosure, alerting acredit check disclosure, alerting a potentially fraudulent transaction,marking a call for follow-up, or alerting to customer churn.

In an implementation, a system is provided. The system includes an agentcomputing device configured to receive a call from a user computingdevice; and a computing device comprising a real-time speech analytics(RTSA) engine configured to monitor and analyze the call in real-time todetect at least one of predetermined words, predetermined phrases, andsentiment, and to guide an interaction during the call using one or moreautomated interventions.

Implementations may include some or all of the following features. Thesystem may further comprise a call recording engine configured tocapture the interaction between a user and an agent during the call; areal-time biometrics engine configured to authenticate the user; aspeech analytics (SA) engine configured to analyze and categorize thecall; and a desktop and process analytics (DPA) engine configured toincorporate desktop events at the agent computing device during thecall. The system may further comprise an output device configured tooutput at least one of an action or an alert generated by the RTSAengine. The RTSA engine comprises a rules module configured to at leastone of receive, determine, set, or maintain a plurality of rulespertaining to the call; a detection module configured to listen to thecall according to the plurality of rules and to detect that one of theplurality of rules is triggered as a match or as an event; an analysismodule configured to analyze the match or the event and determine atleast one of an action or an alert to perform; and an action and alertsmodule configured to receive an instruction from the analysis modulepertaining to the at least one of the action or the alert to perform,and configured to perform the at least one of the action or the alert.The at least one of an action or an alert to perform comprises at leastone of alerting an agent to a condition, alerting the agent computingdevice to the condition, sending an instruction to an output device,sending an indicator to a desktop and process analytics (DPA) engine;providing a knowledge base article or link, alerting a credit checkdisclosure, alerting a credit check disclosure, alerting a potentiallyfraudulent transaction, marking a call for follow-up, or alerting tocustomer churn. The plurality of rules dictate the call to be analyzedand how the call is analyzed, and wherein the plurality of rules definea plurality of keywords to detect and sentiment to detect. The agentcomputing device has at least one of human voice capability or virtualagent voice capability.

In an implementation, a method is provided. The method comprisesmaintaining, at a detection module, a plurality of real-time speechconditions, rules, and triggers; maintaining, at an analysis module, aplurality of real-time actions and alerts to take; receiving a call atan agent computing device; detecting in the call one of the plurality ofreal-time speech conditions, rules, and triggers; and initiating one ormore of the plurality of real-time actions and alerts based on thedetected one of the plurality of real-time speech conditions, rules, andtriggers.

Implementations may include some or all of the following features. Themethod further comprises maintaining a plurality of recording rulespertaining to a call at a rules module, and implementing the pluralityof recording rules on the call. The plurality of real-time speechconditions, rules, and triggers comprises at least one of predeterminedwords, predetermined phrases, and sentiment, to detect during the call.The plurality of real-time actions and alerts comprises at least one ofalerting an agent to a condition, alerting the agent computing device tothe condition, sending an instruction to an output device, sending anindicator to a desktop and process analytics (DPA) engine; providing aknowledge base article or link, alerting a credit check disclosure,alerting a credit check disclosure, alerting a potentially fraudulenttransaction, marking a call for follow-up, or alerting to customerchurn.

Although exemplary implementations may refer to utilizing aspects of thepresently disclosed subject matter in the context of one or morestand-alone computer systems, the subject matter is not so limited, butrather may be implemented in connection with any computing environment,such as a network or distributed computing environment. Still further,aspects of the presently disclosed subject matter may be implemented inor across a plurality of processing chips or devices, and storage maysimilarly be effected across a plurality of devices. Such devices mightinclude personal computers, network servers, and handheld devices, forexample.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed:
 1. A real-time speech analytics (RTSA) system,comprising: a rules module configured to maintain a plurality of rulespertaining to a call between a user computing device and an agentcomputing device; a detection module configured to listen to the callaccording to the plurality of rules and to detect that one of theplurality of rules directed to an event is triggered as a match to anevent; an analysis module configured to analyze the match to the eventand determine at least one of an action or an alert to perform; and anaction and alerts module configured to receive an instruction from theanalysis module pertaining to the at least one of the action or thealert to perform, and configured to perform the at least one of theaction or the alert.
 2. The RTSA system of claim 1, wherein theplurality of rules is directed to a life event discussion scenario, andwhen the detection module detects that the one of the plurality of rulesdirected to the event is triggered, the detection module sends anotification to the analysis module, and the analysis module analyzesthe match, and triggers the action and alerts module to take action,wherein action is triggering an address change knowledge article to bepresented to an agent on the agent computing device.
 3. The RTSA systemof claim 1, wherein the plurality of rules is directed to a medicalcondition, and when the detection module detects words defined for themedical condition, the action and alerts module provides the agentcomputing device with a link to a relevant article in a knowledge base.4. The RTSA system of claim 1, wherein the plurality of rules isdirected to a credit check disclosure scenario, and when the detectionmodule recognizes that a discussion is occurring pertaining to a creditcheck, provides an indicator that the discussion is occurring, anddetermines whether a credit check was discussed, and wherein if thecredit check was not discussed, then a reminder is sent to an agent viathe agent computing device.
 5. The RTSA system of claim 1, wherein theplurality of rules is directed to a credit check disclosure scenario,and when the detection module recognizes that a discussion is occurringpertaining to a credit check, provides a credit check discussionreminder to an agent via the agent computing device.
 6. The RTSA systemof claim 1, wherein the plurality of rules is directed to a risky orpotentially fraudulent transaction scenario, and when the detectionmodule detects words defined for the risky or potentially fraudulenttransaction scenario, the action and alerts module provides anotification to an agent via the agent computing device.
 7. The RTSAsystem of claim 1, wherein the plurality of rules is directed to afollow-up queue for missed first call resolution (FCR) scenario.
 8. TheRTSA system of claim 1, wherein the plurality of rules is directed to acustomer churn and save offer scenario, and when the detection moduledetects words defined for the customer churn and save offer scenario,the action and alerts module guides an agent in real-time via the agentcomputing device with a script and offers to attempt to stop a customerfrom leaving.
 9. The RTSA system of claim 1, wherein the at least one ofthe action or the alert to perform comprises at least one of alerting anagent to a condition, alerting the agent computing device to thecondition, sending an instruction to an output device, or sending anindicator to a desktop and process analytics (DPA) engine.
 10. The RTSAsystem of claim 9, wherein the condition comprises a real-time speechcondition, a rule, or a trigger, and wherein the condition is providedto the detection module by machine learning or artificial intelligence.11. The RTSA system of claim 1, wherein the at least one of the actionor the alert to perform comprises at least one of providing a knowledgebase article or link, alerting a credit check disclosure, alerting acredit check disclosure, alerting a potentially fraudulent transaction,marking a call for follow-up, or alerting to customer churn.
 12. TheRTSA system of claim 1, wherein the at least one of the action or thealert (1) ensures regulatory compliance and compliance with regulationsand policies; (2) increases customer retention; (3) increases firstcontact resolution; (4) increases sales and manage marketing campaigns;or (5) enhances coaching opportunities.
 13. A system comprising: anagent computing device configured to receive a call from a usercomputing device; and a computing device comprising: a real-time speechanalytics (RTSA) engine comprising: a rules module configured tomaintain a plurality of rules pertaining to a call between the usercomputing device and the agent computing device, wherein the pluralityof rules dictate the call to be analyzed and how the call is analyzed,and wherein the plurality of rules define a plurality of keywords todetect and sentiment to detect; a detection module configured to listento the call according to the plurality of rules and to detect that oneof the plurality of rules directed to an event is triggered as a matchto an event; an analysis module configured to analyze the match to theevent and determine at least one of an action or an alert to perform;and an action and alerts module configured to receive an instructionfrom the analysis module pertaining to the at least one of the action orthe alert to perform, and configured to perform the at least one of theaction or the alert.
 14. The system of claim 13, wherein the pluralityof rules is directed to a life event discussion scenario, and when thedetection module detects that the one of the plurality of rules directedto the event is triggered, the detection module sends a notification tothe analysis module, and the analysis module analyzes the match, andtriggers the action and alerts module to take action, wherein action istriggering an address change knowledge article to be presented to anagent on the agent computing device.
 15. The system of claim 13, whereinthe plurality of rules is directed to a medical condition, and when thedetection module detects words defined for the medical condition, theaction and alerts module provides the agent computing device with a linkto a relevant article in a knowledge base.
 16. The system of claim 13,wherein the plurality of rules is directed to a credit check disclosurescenario, and when the detection module recognizes that a discussion isoccurring pertaining to a credit check, provides an indicator that thediscussion is occurring, and determines whether a credit check wasdiscussed, and wherein if the credit check was not discussed, then areminder is sent to an agent via the agent computing device.
 17. Amethod comprising: maintaining, at a rules module, a plurality of rulespertaining to a call between a user computing device and an agentcomputing device; maintaining, at an analysis module, a plurality ofreal-time actions and alerts to take; receiving a call at the agentcomputing device; detecting, at a detection module, in the call that oneof the plurality of rules directed to an event is triggered; analyzing,at the analysis module, the match to the event and determine at leastone of the real-time actions or alerts to perform; and initiating the atleast one of the real-time actions or alerts to perform.
 18. The methodof claim 17, wherein the plurality of rules is directed to a risky orpotentially fraudulent transaction scenario, and when the detectionmodule detects words defined for the risky or potentially fraudulenttransaction scenario, the action and alerts module provides anotification to an agent via the agent computing device.
 19. The methodof claim 17, wherein the plurality of rules is directed to a customerchurn and save offer scenario, and when the detection module detectswords defined for the customer churn and save offer scenario, the actionand alerts module guides an agent in real-time via the agent computingdevice with a script and offers to attempt to stop a customer fromleaving.
 20. The method of claim 17, wherein the at least one of thereal-time actions or alerts (1) ensures regulatory compliance andcompliance with regulations and policies; (2) increases customerretention; (3) increases first contact resolution; (4) increases salesand manage marketing campaigns; or (5) enhances coaching opportunities.